Big Book of R
1
Welcome :)
1.1
Your last-ever bookmark
1.2
Searching
1.3
Contributing
1.4
Contributors
1.5
Licence
1.6
Live stats
1.7
About me
2
New to R? Start here
2.0.1
Book: R for Data Science
2.0.2
Video Course: Getting started with R
3
Book Clubs
3.1
NHS-R community
3.2
R4DS Slack Community
3.3
R-ladies Netherlands - Advanced R by Hadley Wickham
4
Career & Community
4.1
Build Your Career in Data Science
4.2
Getting Started in Data Science
4.3
Twitter for R Programmers
4.4
Twitter for Scientists
4.5
Conversations On Data Science
4.6
Executive Data Science
4.7
Essays on Data Analysis
5
Big Data
5.1
Exploring, Visualizing, and Modeling Big Data with R
5.2
Mastering Spark with R
6
Blogdown
6.1
blogdown: Creating Websites with R Markdown
7
Bookdown
7.1
bookdown: Authoring Books and Technical Documents with R Markdown
7.2
A Minimal Book Example
8
Data Science
8.1
R for Data Science
8.2
R for Data Science Solutions
8.3
Introduction to Data Science
8.4
Data Science: A First Introduction
8.5
Practical Data Science with R, Second Edition
8.6
R Programming for Data Science
8.7
Exploratory Data Analysis… by Roger D. Peng
8.8
edav.info/
8.9
APS 135: Introduction to Exploratory Data Analysis with R
8.10
The Art of Data Science
8.11
The Elements of Data Analytic Style
8.12
Beginning Data Science in R
8.13
Business Intelligence with R
8.14
R Data Science Quick Reference
8.15
Modern Statistics with R
9
Data Visualization
9.1
ggplot2: Elegant Graphics for Data Analysis
9.2
A ggplot2 Tutorial for Beautiful Plotting in R
9.3
ggplot2 in 2
9.4
Data Visualization - A practical introduction
9.5
Data Processing & Visualization
9.6
Data Visualization in R
9.7
Data Visualization with R
9.8
R Graphics Cookbook, 2nd edition
9.9
plotly Interactive web-based data visualization with R, plotly, and shiny
9.10
Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code
9.11
BBC Visual and Data Journalism cookbook for R graphics
9.12
Fundamentals of Data Visualization
9.13
Graphical Data Analysis with R
9.14
JavaScript for R
10
Field specific
10.1
Analyzing Financial and Economic Data with R
10.2
Data Science in Education Using R
10.3
Data Skills for Reproducible Science
10.4
Learning Microeconometrics with R
10.5
Handbook of Regression Modeling in People Analytics
10.6
R Programming with Minecraft
10.7
Social Data Science with R
10.8
The Plain Person’s Guide to Plain Text Social Science
10.9
Technical Foundations of Informatics
10.10
An introduction to quantitative analysis of political data in R
10.11
Machine Learning for Factor Investing
10.12
Introduction to Econometrics with R
10.13
How to be a modern scientist
10.14
Cryptocurrency Research: Open Source R Tutorial
11
Getting, cleaning and wrangling data
11.1
A Beginner’s Guide to Clean Data - beginners-guide-to-clean-data
11.2
21 Recipes for Mining Twitter Data with rtweet
11.3
Text Mining with R
11.4
Spreadsheet Munging Strategies
12
Geospatial
12.1
Geocomputation with R
12.2
Spatial Data Science
12.3
Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
12.4
Introduction to Spatial Data Programming with R
12.5
Predictive Soil Mapping with R
12.6
Using R for Digital Soil Mapping
13
Journalism
13.1
Practical R for Mass Communication and Journalism
13.2
Using R for Data Journalism
14
Life Sciences
14.1
Data Analysis for the Life Sciences
14.2
Modern Statistics for Modern Biology
14.3
Orchestrating Single-Cell Analysis with Bioconductor
14.4
Assigning cell types with SingleR
14.5
Statistics in R for Biodiversity Conservation Paperback
14.6
Computational Genomics with R
14.7
Numerical Ecology with R
15
Machine Learning
15.1
Hands-On Machine Learning with R
15.2
Feature Engineering and Selection: A Practical Approach for Predictive Models
15.3
Interpretable Machine Learning
15.4
Explanatory Model Analysis
15.5
Supervised Machine Learning for Text Analysis in R
15.6
Machine Learning for Factor Investing
15.7
Mathematics and Programming for Machine Learning with R: From the Ground Up 1st Edition, Kindle
15.8
The caret Package
15.9
A Minimal rTorch Book
15.10
Tidy Modeling with R
15.11
mlr3 book
16
Network analysis
16.1
Network Analysis in R Cookbook
17
Reports: R Markdown and knitr
17.1
R Markdown: The Definitive Guide
17.2
R Markdown Cookbook
17.3
Getting used to R, RStudio, and R Markdown
17.4
Report Writing for Data Science in R
17.5
Introduction to R Markdown
17.6
RMarkdown for Scientists
17.7
Pimp my RMD: a few tips for R Markdown
17.8
knitr
17.9
Reproducible Research with R and RStudio
18
R package development
18.1
R packages
18.2
rOpenSci Packages: Development, Maintenance, and Peer Review
18.3
HTTP testing in R
19
R programming
19.1
Modern R with the tidyverse
19.2
stats545 Data wrangling, exploration, and analysis with R
19.3
What They Forgot to Teach You About R
19.4
Field Guide to the R Ecosystem
19.5
YaRrr! The Pirate’s Guide to R
19.6
Advanced R
19.7
Advanced R Solutions
19.8
Efficient R programming
19.9
The Tidyverse Cookbook
19.10
The tidyverse style guide
19.11
Tidyverse design guide
19.12
Tidyverse Skills for Data Science
19.13
Hands-On Programming with R
19.14
The R Language
19.15
R language for programmers
19.16
R Cookbook - 2nd edition
19.17
Cookbook for R
19.18
Tidy evaluation
19.19
The R Inferno
19.20
A sufficient Introduction to R
19.21
Introduction to Programming with R
19.22
Mastering Software Development in R
19.23
Introduction to R - R spatial
19.24
Another Book on Data Science : Learn R and Python in Parallel
19.25
Functional Programming in R
19.26
Advanced Object-Oriented Programming in R
19.27
Metaprogramming in R
19.28
Functional Data Structures in R
19.29
Domain-Specific Languages in R
20
Shiny
20.1
A gRadual intRoduction to Shiny
20.2
Mastering Shiny
20.3
Shiny Production with AWS Book
20.4
Engineering Production-Grade Shiny Apps
20.5
Supplement to Shiny in Production
20.6
Outstanding User Interfaces with Shiny
20.7
JavaScript for R
21
Sport analytics
21.1
Basketball Data Science with Applications in R
21.2
Exploring Baseball Data with R
21.3
Coding for sports analytics: get started resources
22
Statistics
22.1
Answering questions with data
22.2
Bayes rules!
22.3
A Business Analyst’s Introduction to Business Analytics: Intro to Bayesian Business Analytics in the R Ecosystem
22.4
Common statistical tests are linear models: a work through
22.5
Handbook of Regression Modeling in People Analytics
22.6
Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6.1)
22.7
Mixed Models with R : Getting started with random effects
22.8
An Introduction to Statistical and Data Sciences via R
22.9
Statistical Rethinking
22.10
Statistical Rethinking with brms, ggplot2, and the tidyverse
22.11
OpenIntro Statistics
22.12
Introduction to Modern Statistics
22.13
Statistical inference for data science
22.14
Statistics (The Easier Way) With R, 3rd. Ed. (TIDYVERSION)
22.15
End-to-End Solved Problems With R: a catalog of 26 examples using statistical inference
22.16
Statistics and Data with R: An Applied Approach Through Examples
22.17
TEACUPS, GIRAFFES, & STATISTICS
22.18
Modern Statistics with R
23
Time Series Analysis and Forecasting
23.1
Forecasting: Principles and Practice
23.2
Time Series Analysis and Its Applications
23.3
Hands-On Time Series Analysis with R
23.4
Practical Time Series Forecasting with R: A Hands-On Guide
23.5
Time Series - A Data Analysis Approach Using R
23.6
Applied Time Series Analysis for Fisheries and Environmental Sciences
23.7
Fisheries Catch Forecasting
24
Teaching
24.1
Data Science in a Box
24.2
rstudio4edu
24.3
Teaching Tech Together
25
Text analysis
25.1
Text Mining with R
25.2
Supervised Machine Learning for Text Analysis in R
26
Version control
26.1
Happy Git and GitHub for the useR
26.2
Github actions with R
26.3
Github learning lab
26.4
The Beginner’s Guide to Git and GitHub
27
Workflow
27.1
Agile Data Science with R
27.2
The Data Validation Cookbook
27.3
How I Use R
27.4
Github actions with R
27.5
The targets R Package User Manual
28
Other compendiums
28.1
Bookdown archive
28.2
Use R! Springer series
28.3
Data Science with R: A Resource Compendium
28.4
R on the web
28.5
CRAN doc collections
Published with bookdown
Big Book of R
16
Network analysis
16.1
Network Analysis in R Cookbook
Sacha Epskamp
http://sachaepskamp.com/files/Cookbook.html